Keywords
association rules, forensic computing, data mining, link analysis, predictive models
Abstract
Data mining offers a potentially powerful method for analyzing the large data sets that are typically found in forensic computing (FC) investigations to discover useful and previously unknown patterns within the data. The contribution of this paper is an innovative and rigorous data mining methodology that enables effective search of large volumes of complex data to discover offender profiles. These profiles are based on association rules, which are computationally sound, flexible, easily interpreted, and provide a ready set of data for refinement via predictive models. Methodology incorporates link analysis and creation of predictive models based on association rule input.
BYU ScholarsArchive Citation
Hansen, James V.; Lowry, Paul Benjamin; and Meservy, Rayman D., "Data Mining of Forensic Association Rules" (2003). Faculty Publications. 3242.
https://scholarsarchive.byu.edu/facpub/3242
Document Type
Peer-Reviewed Article
Publication Date
2003
Permanent URL
http://hdl.lib.byu.edu/1877/6053
Language
English
College
Marriott School of Management
Department
Information Systems